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    <title>浏阳德塔软件开发有限公司 女娲计划</title>
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    <br/>第一章_德塔自然语言图灵系统
    <br/> 作者: 罗瑶光, Author:Yaoguang.Luo<br/>
    <br/> 基础应用: 元基催化与肽计算 编译机的语言分析机
    <br/>
    分词<br/>
    <br/>

    <br/>
    <br/> 1 德塔的分词是一种前序《排队论》逐字遍历文字索引, 通过索引中的词汇匹配 按长度进行提取
    , 然后将提取的词汇串 进行词性切分的过程. refer page 12 ~

    <br/>
    <br/>2 德塔的分词文字索引采用关联分类生成小文件map集(词性map, 词长map, 词类map),
    进行整体加速, 作为一个催化细化过程. refer page 44, 54, 92,

    <br/>
    <br/> 3 德塔的词汇匹配目前有多个国家语言字符集, 可统一, 可拆分, 目前最大划分处理长度为
    4, 划分切词采用动态 类似CNN 卷积(遍历pos函数语句的内核计算, 非卷积的积分叠加计算)
    StringBuilder核做POS识别. refer page 45, 119, 120,

    <br/>
    <br/> 4 德塔的词性切分按照4字词 3字词 2字词 单字 进行逐级按词汇的 POS搭配语法模式进行归纳,
    按文本的POS出现频率进行流水阀门方式优化. refer page 97, 116,

    <br/>
    <br/> Deta parser, a sentence and word segmentational marching tool
    (NERO-NLP-POS), was based on an in-order sequence verbals computation,
    the computer monitored the humanoid specification, to read articles,
    sentences by each word one by one as the river flows, then cut the word
    link list by using stop method such as the index length recognizing,
    word dictionary marching, then extracted the pre-materials for the
    continuing POS (part of speech) process.
    <br/>
    <br/> At the first. The author built a lot of association maps and
    classification sets, which did a nice verbal data storage of all kinds
    of the literary otho corpus (NERO), for instance, POS maps, word length
    maps and word type maps, to better catalyz the system-tuning of
    accelerations.
    <br/>
    <br/> Each lexical map could make a combination, classification and
    definition, the author used the StringBuilder function to do the word
    segmentation by non-conventional different kernel computations. Like
    the water sequence flowed from top to bottom, from left to right, then
    gathered statistics of the frequency-prototype usages, made the queued
    optimizations at the same time and catalyzed the system-tuning of
    accelerations.
    <br/>
    <br/> The word segmentation and Its POS cut ways by following 4
    Chinese chars’ words, 3 chars’ words, 2 chars’ words to loop check the
    POS lexical dictionary-map storages. Kernel computations work for
    finding a marching verbals word, then returned the response to the NLP
    engine.
    <br/> Yaoguang.Luo<br/>
    <br/>
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         alt="浏阳德塔软件开发有限公司,罗瑶光"/>
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    <br/> (德塔分词逻辑, 已经纠正红色字 ‘卷积’改为‘内核’, 因为第四修订版本已包含卷积两字,
    ppt所有书中的原图纠正内容统一更新在第5版, 罗瑶光)

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